It is known in the prior art to use vehicles as probes for measuring traffic conditions in real-time. Individual vehicles provide “floating car data,” such as, for example, the vehicle's time, speed, position, and heading, which can be used to estimate travel time and traffic speed, and which can in turn be used to alert other operators to an approaching condition variance, as an online indicator of road network status, as a basis for detecting incidents, or as input for a dynamic route guidance system.
Prior art probe vehicle systems typically include a plurality of probe vehicles; technology for determining each probe vehicle location, such as, for example, a system using orbiting satellites, such as the Global Positioning System (GPS), a system using cellular telephones, or a system using radio-frequency identification (RFID); and a wireless communication system for allowing communication between the vehicles and a traffic information center (TIC). Typically, the center receives and processes the data generated by the probe vehicles, and then transmits a signal based on the data to a plurality of receiving vehicles, which may further include non-probe vehicles.
Constant communication between the probe vehicles and the center, however, requires the storage of a voluminous amount of data at the center. As additional vehicles join the system, other scalability concerns are presented. First, the system requires constant communication between an exceedingly large number of probe vehicles and the center to maintain an entire map database of traffic information. A substantial data processing capacity is necessary at the center to process the large volume of data in real-time. Finally, typical communication means are similarly impacted by the addition of vehicles, and must be sized accordingly, even though communication quantities are often well below peak.
These concerns reflect the inverse proportionality of capacity and efficiency in central processing systems, and the need in the art for a traffic information system that reduces data storage and processing requirements at the center.